SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3031–3040 of 4891 papers

TitleStatusHype
Studying K-FAC Heuristics by Viewing Adam through a Second-Order LensCode0
Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency—0
Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot FillingCode0
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation—0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
EDGE++: Improved Training and Sampling of EDGE—0
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging DataCode0
Online energy management system for a fuel cell/battery hybrid system with multiple fuel cell stacks—0
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning—0
Accelerated sparse Kernel Spectral Clustering for large scale data clustering problemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified